How much does SF know about how sure it is about best move. Shouldn't this be based on some legal tree exploration (in game) statistics, and some external referential somewhere... I am curious about shared, preferably reproducible statistics of the kind...
an engin that has own ways to giving a score and the confidence on its score... That would be great.. Does it already exist? what are the assumptions under which such feature make sense.?
but yes such measure might be helping in combining sub-optimal expert "systems" so that each compensate the other over each other biases or poor coverage (with lower confidence), if we are lucky and the different engine complement each others....
I have seen a recent result about such sub-optimal expert combinations beating even better solo experts. . the sum being better than the parts.. because the weak region of expertise are not the same for all the parts of the combination. I hope i did not say more than the paper... trying to make this generally meaningful..
It might be better than LC0, which has more end points in its "bad games" in early phases, lots of fools mates, and which deep games might come from better play batches, which might not be as exploring over legal set (RL dilemna). Maybe that explanation is not right, but it is something that i did hear too.. that lc0 is not as good in endgame as in openings.. it has tactical holes.. But that does not make SF good at endgames...
I trust human ELOs to represent a better coverage of chess. because of more humans playing chess than engine programmers.. (who also will share a winning recipe across compatible design engines... LMR and etc....).
more different biases of experience confronting each other in well mixed pairing systems.. And we can count on human error, for not being able to sustain cooperative and conformist play for too long... so that the biases are likely to be all represented in some ELO measure over many games in such human player pools...
For engines, given the small design "gene" pool, we would not know if all of them were competing over the same restricted region of chess space. Unless some outlier engine fast enough to not have that same small region predilection.. The engine would have to be both less biased AND fast enough to beat the other type of design which made a career out of speed improvements which are enough for ELO to keep improving.
This does not mean we should not try to get better by combining the 2 very different species of engine. based on above hypothesis. we might get lucky...
How much does SF know about how sure it is about best move. Shouldn't this be based on some legal tree exploration (in game) statistics, and some external referential somewhere... I am curious about shared, preferably reproducible statistics of the kind...
an engin that has own ways to giving a score and the confidence on its score... That would be great.. Does it already exist? what are the assumptions under which such feature make sense.?
but yes such measure might be helping in combining sub-optimal expert "systems" so that each compensate the other over each other biases or poor coverage (with lower confidence), if we are lucky and the different engine complement each others....
I have seen a recent result about such sub-optimal expert combinations beating even better solo experts. . the sum being better than the parts.. because the weak region of expertise are not the same for all the parts of the combination. I hope i did not say more than the paper... trying to make this generally meaningful..
It might be better than LC0, which has more end points in its "bad games" in early phases, lots of fools mates, and which deep games might come from better play batches, which might not be as exploring over legal set (RL dilemna). Maybe that explanation is not right, but it is something that i did hear too.. that lc0 is not as good in endgame as in openings.. it has tactical holes.. But that does not make SF good at endgames...
I trust human ELOs to represent a better coverage of chess. because of more humans playing chess than engine programmers.. (who also will share a winning recipe across compatible design engines... LMR and etc....).
more different biases of experience confronting each other in well mixed pairing systems.. And we can count on human error, for not being able to sustain cooperative and conformist play for too long... so that the biases are likely to be all represented in some ELO measure over many games in such human player pools...
For engines, given the small design "gene" pool, we would not know if all of them were competing over the same restricted region of chess space. Unless some outlier engine fast enough to not have that same small region predilection.. The engine would have to be both less biased AND fast enough to beat the other type of design which made a career out of speed improvements which are enough for ELO to keep improving.
This does not mean we should not try to get better by combining the 2 very different species of engine. based on above hypothesis. we might get lucky...